The objective of the text-to-image synthesis problem is to generate high-quality images from the specific text descriptions. It is a fundamental problem with a wide range of practical applications, including art generation, image editing, and computer-aided design. Most recently proposed text-to-image synthesis methods [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]}, [16]}, [17]}, [18]}
are based on Generative Adversarial Networks (GANs) [19]}. Conditioned on the text descriptions, the GAN-based models can generate realistic images with consistent semantic meaning. In practice, one image is associated to multiple captions in the datasets. These text descriptions annotated by humans for the same image are highly subjective and diverse in terms of contents and choice of words. Additionally, some text descriptions do not even provide sufficient semantic information to guide the image generation. The
linguistic variance and inadequacy between the captions of the identical
image leads to the synthetic images conditioned on them
deviating from the ground truth.
